SRAF: A Service-Aware Resource Allocation Framework for VM Management in Mobile Data Networks

Service latency and resource utilization are the key factors which limit the development of mobile data networks. To this end, we present a service-aware resource allocation framework, called SRAF, to allocate the basic resources by managing virtual machine (VM). In SRAF, we design two new methods for better virtual machine (VM) management. Firstly, we propose the self-learning classification algorithm (SCA) which executes the service request classification. Then, we use the classification results to schedule different types of VMs. Secondly, we design a sharing mode to jointly execute service requests, which can share the CPU and bandwidth simultaneously. In order to enhance the utilization of resources with the sharing mode, we also design two scaling algorithms, i.e., the horizontal scaling and the vertical scaling, which execute the operation of resource-level scaling and VM-level scaling, respectively. Furthermore, to enhance the stability of SRAF and avoid the frequent operation of scaling, we introduce a Markov decision process (MDP) to control VM migration. The experimental results reveal that SRAF greatly reduces service latency and enhances resource utilization. In addition, SRAF also has a good performance on stability and robustness for different situations of congestion